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Neural circuits can be reconstructed from brain images acquired by serialsection electron microscopy. Image analysis has been performed by manual laborfor half a century, and efforts at automation date back almost as far.Convolutional nets were first applied to neuronal boundary detection a dozenyears ago, and have now achieved impressive accuracy on clean images. Robusthandling of image defects is a major outstanding challenge. Convolutional netsare also being employed for other tasks in neural circuit reconstruction:finding synapses and identifying synaptic partners, extending or pruningneuronal reconstructions, and aligning serial section images to create a 3Dimage stack. Computational systems are being engineered to handle petavoxelimages of cubic millimeter brain volumes.

Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy